Addressing Challenges of L۲ Grammar Learning with a Focus on English Relative Clauses: AI-supported Language Learning

The present study investigates the challenges of learning English grammar with a focus on the syntactic analysis of relative clauses (RCs) in Persian in contrast to English to identify the most common errors made by Persian learners of English. In addition, it aims to enhance L۲ grammar learning and overcome challenges using AI-assisted tools such as Wordtune, Instatext, and ChatGPT in classroom activities. The quantitative data were collected through the RC tests adapted from the models used by Izumi (۲۰۰۳), comprising three test types: sentence combination, interpretation, and grammaticality judgment. These tests were administered before and after the implementation of AI-powered strategies. The result of the tests in intermediate learners revealed that the most recurrent interlingual error was “the use of object pronouns” instead of gaps, while the challenges in “RC reduction” were among the most common intralingual errors. The findings highlight not only the major differences in RC structures between the two languages but also present an innovative approach that uses AI to address these challenges, offering insights for teachers and instructors. Addressing such errors and utilizing technological advances can pave the way for learners and teachers to have more effective learning and teaching strategies.

کلیدواژه ها:

artificial intelligence ، Syntax ، Relative clauses ، AI-Assisted Language Learning